This is the report of the analysis made for the paper (TITLE) AND AUTHORS. INSERT ABSTRACT
Importing data and filtering out those genes with cpm lesser than 1. We use the filtered.data method in NOISeq package.
countMatrix <- ReadDataFrameFromTsv(file.name.path="../../data/refSEQ_countMatrix.txt")
## ../../data/refSEQ_countMatrix.txt read from disk!
# head(countMatrix)
designMatrix <- ReadDataFrameFromTsv(file.name.path="../../design/all_samples_short_names_noRS2HC7.tsv")
## ../../design/all_samples_short_names_noRS2HC7.tsv read from disk!
# head(designMatrix)
filteredCountsProp <- filterLowCounts(counts.dataframe=countMatrix,
is.normalized=FALSE,
design.dataframe=designMatrix,
cond.col.name="gcondition",
method.type="Proportion")
## features dimensions before normalization: 27179
## Filtering out low count features...
## 14454 features are to be kept for differential expression analysis with filtering method 3
PCA Plot of filtered not-normalized data.
PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp),
design.matrix=designMatrix,
shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Prop-Un-Norm")
## [1] TRUE
Loading Negative Control Genes to normalize data
library(readxl)
sd.ctrls <- read_excel(path="../../data/controls/Additional File 4 full list of BMC genomics SD&RS2.xlsx", sheet=1)
sd.ctrls <- sd.ctrls[order(sd.ctrls$adj.P.Val),]
sd.neg.ctrls <- sd.ctrls[sd.ctrls$adj.P.Val > 0.9, ]
sd.neg.ctrls <- sd.neg.ctrls$`MGI Symbol`
sd.neg.ctrls <- sd.neg.ctrls[-which(is.na(sd.neg.ctrls))]
int.neg.ctrls <- sd.neg.ctrls
int.neg.ctrls <- unique(int.neg.ctrls)
neg.map <- convertGenesViaMouseDb(gene.list=int.neg.ctrls, fromType="SYMBOL",
"ENTREZID")
# sum(is.na(neg.map$ENTREZID))
neg.ctrls.entrez <- as.character(neg.map$ENTREZID)
ind.ctrls <- which(rownames(filteredCountsProp) %in% neg.ctrls.entrez)
counts.neg.ctrls <- filteredCountsProp[ind.ctrls,]
Loading Positive Control Genes to detect them during the differential expression step.
## sleep deprivation
sd.lit.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx",
sheet=1)
colnames(sd.lit.pos.ctrls) <- sd.lit.pos.ctrls[1,]
sd.lit.pos.ctrls <- sd.lit.pos.ctrls[-1,]
sd.est.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx",
sheet=3)
sd.pos.ctrls <- cbind(sd.est.pos.ctrls$`MGI Symbol`, "est")
sd.pos.ctrls <- rbind(sd.pos.ctrls, cbind(sd.lit.pos.ctrls$Gene, "lit"))
sd.pos.ctrls <- sd.pos.ctrls[-which(duplicated(sd.pos.ctrls[,1])),]
sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls[,1])),]
Normalizing data with TMM, as implemented in edgeR package, and plotting a PCA and an RLE plot of them.
normPropCountsUqua <- NormalizeData(data.to.normalize=filteredCountsProp,
norm.type="tmm",
design.matrix=designMatrix)
PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua),
design.matrix=designMatrix, shapeColname="condition",
colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=TRUE, show.plot.flag=TRUE,
prefix.plot="TMM-Norm")
## [1] TRUE
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(as.matrix(normPropCountsUqua), outline=FALSE, col=pal[designMatrix$gcondition])
Applying a RUVs method of RUVSeq package on normalized data, in order to adjust the counts for the unwanted variation. And of corse we plot a PCA and an RLE plot on these data.
library(RUVSeq)
neg.ctrl.list <- rownames(counts.neg.ctrls)
groups <- makeGroups(designMatrix$gcondition)
ruvedSExprData <- RUVs(as.matrix(round(normPropCountsUqua)), cIdx=neg.ctrl.list,
scIdx=groups, k=5)
normExprData <- ruvedSExprData$normalizedCounts
ggp <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData),
design.matrix=designMatrix, shapeColname="condition",
colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=FALSE, show.plot.flag=FALSE, save.plot=FALSE,
prefix.plot=NULL)
## [1] FALSE
ggplotly(ggp)
dir.create("plots")
save_plot(filename="plots/PCA.pdf", plot=ggp)
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(normExprData, outline=FALSE, col=pal[designMatrix$gcondition])
Making differential expression analysis with edgeR package on four different contrasts.
Here is a brief legend: * WTHC5: Wild Type Home Cage Control 5 days * WTSD5: Wild Type Sleep Deprivation 5 days. * KOHC5: Knock Out Home Cage Control 5 days. * KOSD5: Knock Out Sleep Deprivation 5 days.
padj.thr <- 0.05
venn.padgj.thr <- 0.1
desMat <- cbind(designMatrix, ruvedSExprData$W)
colnames(desMat) <- c(colnames(designMatrix), colnames(ruvedSExprData$W))
cc <- c("WTSD5 - WTHC5", "KOHC5 - WTHC5",
"KOSD5 - WTSD5", "KOSD5 - KOHC5")
rescList1 <- applyEdgeR(counts=filteredCountsProp, design.matrix=desMat,
factors.column="gcondition",
weight.columns=c("W_1", "W_2", "W_3", "W_4", "W_5"),
contrasts=cc, useIntercept=FALSE, p.threshold=1,
is.normalized=FALSE, verbose=TRUE)
names <- names(rescList1)
rescList1 <- lapply(seq_along(rescList1), function(i)
{
attachMeans(normalized.counts=normExprData, design.matrix=desMat,
factor.column="gcondition", contrast.name=names(rescList1)[i],
de.results=rescList1[[i]])
})
names(rescList1) <- names
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[1]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[1])
A volcano plot of differential expressed genes.
res.o.map1 <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[1]]),
fromType="ENTREZID")
res.o <- attachGeneColumnToDf(mainDf=rescList1[[1]],
genesMap=res.o.map1,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0(names(rescList1)[1], "_edgeR"))
vp <- luciaVolcanoPlot(res.o, sd.pos.ctrls, prefix=names(rescList1)[1],
threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[1]
ddetable <- detable
tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.df <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
colnames(pos.df) <- c("total_p.ctrl", "p.ctrl_de_mapped",
"p.ctrl_notde_mapped")
rownames(pos.df) <- names(rescList1)[1]
wt <- res.o[which(res.o$FDR < padj.thr),]
wt.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[2]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[2])
A volcano plot of differential expressed genes.
rs2.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[2]]),
fromType="ENTREZID")
res.rs2.o <- attachGeneColumnToDf(mainDf=rescList1[[2]],
genesMap=rs2.o.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.rs2.o,
file.name.path=paste0(names(rescList1)[2], "_edgeR"))
vp <- luciaVolcanoPlot(res.rs2.o, positive.controls.df=NULL,
prefix=names(rescList1)[2],
threshold=padj.thr)
ggplotly(vp)
de <- sum(res.rs2.o$FDR < padj.thr)
nde <- sum(res.rs2.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[2]
ddetable <- rbind(ddetable, detable)
kowthc5 <- res.rs2.o[which(res.rs2.o$FDR < padj.thr),]
kowthc5.sign.genes.entrez <- rownames(res.rs2.o)[which(res.rs2.o$FDR < venn.padgj.thr)]
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[3]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[3])
A volcano plot of differential expressed genes.
res.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[3]]),
fromType="ENTREZID")
res.o <- attachGeneColumnToDf(mainDf=rescList1[[3]],
genesMap=res.o.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0(names(rescList1)[3], "_edgeR"))
vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls,
prefix=names(rescList1)[3], threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[3]
ddetable <- rbind(ddetable, detable)
tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[3]
pos.df <- rbind(pos.df, pos.dff)
kowtsd5 <- res.o[which(res.o$FDR < padj.thr),]
kowtsd5.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[4]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[4])
A volcano plot of differential expressed genes.
res.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[4]]),
fromType="ENTREZID")
res.o <- attachGeneColumnToDf(mainDf=rescList1[[4]],
genesMap=res.o.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0(names(rescList1)[4], "_edgeR"))
vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls,
prefix=names(rescList1)[4], threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[4]
ddetable <- rbind(ddetable, detable)
tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[2]
pos.df <- rbind(pos.df, pos.dff)
ko <- res.o[which(res.o$FDR < padj.thr),]
ko.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]
We present a summarization of the results. The first table is a summarization on how many genes are Differentially Expressed. The second table explains on the first column how many positive controls we have, on the second column how many positive controls have been identified over the differentially expressed genes, and, finally, on the third column how many positive controls have beed identified on the NOT differentially expressed genes.
ddetable
## de nde
## WTSD5 - WTHC5 5650 8804
## KOHC5 - WTHC5 39 14415
## KOSD5 - WTSD5 82 14372
## KOSD5 - KOHC5 5772 8682
pos.df
## total_p.ctrl p.ctrl_de_mapped p.ctrl_notde_mapped
## WTSD5 - WTHC5 579 454 102
## KOSD5 - WTSD5 579 4 552
## KOHC5 - WTHC5 579 452 104
We take the results of two contrasts. Knock Out Sleed Deprivation VS Wild Type Sleep Deprivation and Knock Out Home Cage control VS Wild Type Home Cage Controls . And plot the results in a Venn Diagram
source("../../R/venn2.R")
gene.map <- convertGenesViaMouseDb(gene.list=rownames(normExprData),
fromType="ENTREZID", toType="SYMBOL")
venn <- Venn2de(x=kowthc5.sign.genes.entrez, y=kowtsd5.sign.genes.entrez,
label1="KOHC5_WTHC5", label2="KOSD5_WTSD5",
title="KOHC5_WTHC5 venn KOSD5_WTSD5", plot.dir="./",
conversion.map=gene.map)
An heatmap of the union of the genes identified.
source("../../R/heatmapFunctions.R")
de.genes.entr <- union(rownames(venn$int), rownames(venn$XnoY))
de.genes.entr <- union(de.genes.entr, rownames(venn$YnoX))
gene.map <- convertGenesViaMouseDb(gene.list=de.genes.entr,
fromType="ENTREZID")
de.genes.symb <- attachGeneColumnToDf(as.data.frame(de.genes.entr,
row.names=de.genes.entr),
genesMap=gene.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
# de.genes.symb[which(is.na(de.genes.symb$gene)),]
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="100039826")] <- "Gm2444" ## not annotated in ncbi
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="210541")] <- "Entrez:210541" ## not annotated in ncbi
de.genes.counts <- normExprData[match(de.genes.symb$de.genes.entr, rownames(normExprData)),]
rownames(de.genes.counts) <- de.genes.symb$gene
de.gene.means <- computeGeneMeansOverGroups(counts=de.genes.counts,
design=designMatrix, groupColumn="gcondition")
library(gplots)
library(clusterExperiment)
color.palette = clusterExperiment::seqPal3#c("black", "yellow")
pal <- colorRampPalette(color.palette)(n = 1000)
# table(filter)
library(pheatmap)
filter2 <- rowMeans(de.gene.means)>0
ph1 <- pheatmap(log(de.gene.means[filter2,]+1), cluster_cols=FALSE, scale="row", color=pal, border_color=NA, fontsize_row=5)
save_pheatmap_pdf(filename="plots/heatmap_union_genes.pdf", plot=ph1)
## RStudioGD
## 2
Heatmap of a group of genes which present inverse trends between Wild Type and Knock Out conditions.
filter <- apply(de.gene.means, 1, function(x) log(x[4]/x[3]) * log(x[2]/x[1]) < 0)
filter[is.na(filter)] <- FALSE
ph2 <- pheatmap(log(de.gene.means[filter,]+1), cluster_cols=FALSE, scale="row", color=pal, border_color=NA, fontsize_row=8)
save_pheatmap_pdf(filename="plots/heatmap_genes_two_trends.pdf", plot=ph2)
## RStudioGD
## 2
The trends of the genes identified in the second heatmap.
source("../../R/plotGeneProfile.R")
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gm7984",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gm7984", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Mgat4b",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Mgat4b", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ano6",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ano6", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Zbtb21",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Zbtb21", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Sfxn1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Sfxn1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Plxnb2",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Plxnb2", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gng4",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gng4", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Slc6a13",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Slc6a13", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Pdyn",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Pdyn", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Reln",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Reln", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Rpl29",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Rpl29", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ryr1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ryr1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Rnd1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Rnd1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Plekha6",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Plekha6", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Mapk1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Mapk1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Slc7a11",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Slc7a11", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gprin1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gprin1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Cttnbp2",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Cttnbp2", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Jmy",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Jmy", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Nek6",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nek6", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Dusp10",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Dusp10", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Zmynd19",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Zmynd19", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Kcnv1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Kcnv1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ddit4l",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ddit4l", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Fggy",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Fggy", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Sytl1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Sytl1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Bmp1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Bmp1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Otof",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Otof", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Syt7",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Syt7", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Chia1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Chia1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Tuba1c",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Tuba1c", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Olfr287",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Olfr287", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gm7984",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gm7984", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Mgat4b",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Mgat4b", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gprin1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gprin1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Atp6v0c",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Atp6v0c", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Jun",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Jun", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ddit4l",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Ddit4l", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Pdyn",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Pdyn", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Sema3a",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Sema3a", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gng4",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gng4", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Reln",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Reln", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Kcnv1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Kcnv1", "_gene_profile.pdf"), plot=g)
Profiles of some known genes.
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Nr1d1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nr1d1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Per3",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Per3", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Fabp7",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Fabp7", "_gene_profile.pdf"), plot=g)
# g <- geneGroupProfile(normalized.counts=normExprData, design.matrix=designMatrix,
# gene.names=c("Nr1d1", "Fabp7", "Per3"),
# res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
# ggplotly(g)